Improving Learning Performance of Support Vector Machine using the Kernel Relaxation and the Dynamic Momentum


The KIPS Transactions:PartB , Vol. 9, No. 6, pp. 735-744, Dec. 2002
10.3745/KIPSTB.2002.9.6.735,   PDF Download:

Abstract

This paper proposes learning performance improvement of support vector machine using the kernel relaxation and the dynamic momentum. The dynamic momentum is reflected to different momentum according to current state. While static momentum is equally influenced on the whole, the proposed dynamic momentum algorithm can control to the convergence rate and performance according to the change of the dynamic momentum by training. The proposed algorithm has been applied to the kernel relaxation as the new sequential learning method of support vector machine presented recently. The proposed algorithm has been applied to the SONAR data which is used to the standard classification problems for evaluating neural network. The simulation results of proposed algorithm have better the convergence rate and performance than those using kernel relaxation and static momentum, respectively.


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Cite this article
[IEEE Style]
E. M. Kim and B. H. Lee, "Improving Learning Performance of Support Vector Machine using the Kernel Relaxation and the Dynamic Momentum," The KIPS Transactions:PartB , vol. 9, no. 6, pp. 735-744, 2002. DOI: 10.3745/KIPSTB.2002.9.6.735.

[ACM Style]
Eun Mi Kim and Bae Ho Lee. 2002. Improving Learning Performance of Support Vector Machine using the Kernel Relaxation and the Dynamic Momentum. The KIPS Transactions:PartB , 9, 6, (2002), 735-744. DOI: 10.3745/KIPSTB.2002.9.6.735.